• Journal of electromagnetic engineering and science
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• 제14권4호
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• pp.405-410
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• 2014

In this paper, we compare the quality of images reconstructed by a conventional delayed-sum (DS) algorithm and radiation pattern-based DS algorithm. In order to evaluate the quality of images, we apply the target-to-clutter ratio (TCR), which is commonly used in synthetic aperture radar (SAR) image assessment. The radiation pattern-based DS algorithm enhances the TCR of the image by focusing the target signals and preventing contamination of the radar scene. We first consider synthetic data obtained through GprMax2D/3D, a finite-difference time-domain (FDTD) forward solver. Experimental data of a 2-GHz bandwidth stepped-frequency signal are collected using a vector network analyzer (VNA) in an anechoic chamber setup. The radiation pattern-based DS algorithm shows a 6.7-dB higher TCR compared to the conventional DS algorithm.

• 제어로봇시스템학회논문지
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• 제7권12호
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• pp.985-992
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• 2001

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

• Industrial Engineering and Management Systems
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• 제8권1호
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• pp.22-28
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• 2009

We consider facility layout problems, where mn facility units are assigned into mn cells. These cells are arranged into a rectangular pattern with m rows and n columns. In order to solve this cell type facility layout problem, many approximation algorithms with improved local search methods were studied because it was quite difficult to find exact optimum of such problem in case of large size problem. In this paper, new algorithms based on Simulated Annealing (SA) method with two neighborhood generation methods are proposed. The new neighborhood generation method adopts the exchanging operation of facility units in accordance with adjacent preference. For evaluating the performance of the neighborhood generation method, three algorithms, previous SA algorithm with random 2-opt neighborhood generation method, the SA-based algorithm with the new neighborhood generation method (SA1) and the SA-based algorithm with probabilistic selection of random 2-opt and the new neighborhood generation method (SA2), are developed and compared by experiment of solving same example problem. In case of numeric examples with problem type 1 (the optimum layout is given), SA1 algorithm could find excellent layout than other algorithms. However, in case of problem type 2 (random-prepared and optimum-unknown problem), SA2 was excellent more than other algorithms.

• 한국통신학회논문지
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• 제25권5B호
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• pp.830-841
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• 2000

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-1, we may compute the updated estimate of this vector at iteration a upon the arrival of new data. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. In this paper, we propose new tap weight updated RLS algorithm in adaptive transversal filter with data-recycling buffer structure. We prove that convergence speed of learning curve of RLS algorithm with data-recycling buffer is faster than it of exiting RL algorithm to mean square error versus iteration number. Also the resulting rate of convergence is typically an order of magnitude faster than the simple LMS algorithm. We show that the number of desired sample is portion to increase to converge the specified value from the three dimension simulation result of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of convergence character in performance, is achieved at the (B+1)times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.

• 대한전자공학회논문지SP
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• 제43권1호
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• pp.75-84
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• 2006

NTSS 알고리즘 (New Three-Step Search Algorithm)은 대표적인 고속 블록 정합 알고리즘(Block Matching Algorithm: BMA)의 하나인 TSS 알고리즘 (Three-Step Search Algorithm)에 동영상이 갖는 중앙 편향적(Center-Biased) 특성을 반영한 방법이다. 그러나 NTSS는 움직임이 작은 동영상인 경우에는 TSS보다 개선된 성능을 보여주지만, 움직임이 큰 동영상에 대해서는 TSS와 큰 차이가 없으며, 탐색범위가 커질수록 오히려 성능이 떨어지는 단점이 있다 본 논문에서는 움직임 벡터의 특성에 따라 적응적으로 탐색범위를 결정하여 탐색범위의 증가로 발생되는 NTSS의 단점을 보완함으로써 움직임이 큰 동영상에 대해서도 향상된 성능을 갖는 방법을 제안한다. 제안한 방법을 적용하였을 때 움직임이 작은 동영상에서는 기존 NTSS 방법과 동일한 화질을 유지하면서 움직임이 큰 동영상에서는 최대 0.5dB 이상 성능이 개선되었다.

• Journal of Electrical Engineering and Technology
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• 제8권4호
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• pp.889-898
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• 2013

The shortest-path searching algorithm must not only find a global solution to the destination, but also solve a turn penalty problem (TPP) in an urban road transportation network (URTN). Although the Dijkstra algorithm (DA) as a representative node-based algorithm secures a global solution to the shortest path search (SPS) in the URTN by visiting all the possible paths to the destination, the DA does not solve the TPP and the slow execution speed problem (SEP) because it must search for the temporary minimum cost node. Potts and Oliver solved the TPP by modifying the visiting unit from a node to the link type of a tree-building algorithm like the DA. The Multi Tree Building Algorithm (MTBA), classified as a representative Link Based Algorithm (LBA), does not extricate the SEP because the MTBA must search many of the origin and destination links as well as the candidate links in order to find the SPS. In this paper, we propose a new Link-Based Single Tree Building Algorithm in order to reduce the SEP of the MTBA by applying the breaking rule to the LBA and also prove its usefulness by comparing the proposed with other algorithms such as the node-based DA and the link-based MTBA for the error rates and execution speeds.

• 대한전자공학회논문지SD
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• 제39권3호
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• pp.20-33
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• 2002

현재 다양한 분야에서 이중 포트 메모리의 사용이 증가함에 따라서 이중 포트 메모리의 고장을 진단하기 위한 효율적인 고장 진단 알고리듬의 필_도성이 증대되고 있다. 따라서 본 논문에서는 이중 포트 메모리에서의 효율적인 고장 진단 알고리듬을 제시하여 이중 포트 메모리에서 발생하는 거의 모든 종류의 고장에 대한 진단을 가능하게 한다. 또한 진단 과정에서 착오를 일으키지 않고 다양한 고장 모델을 구별하며 고장과 관련된 위치를 정확하게 확인하는 것이 가능하다. 새로운 진단 알고리듬을 사용함으로서 이중 포트 메모리에서의 고장 진단과정은 효과적으로 수행될 수 있으며 이전의 다른 연구들과의 성능 평가를 통해 효율성을 확인할 수 있다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1868-1870
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• 2006

Neural network is used in many fields of control systems, but input-output patterns of a control system are not easy to be obtained and by using as single feedback neural network controller. And also it is difficult to get a satisfied performance when the changes of rapid load and disturbance are applied. To resolve those problems, this paper proposes a new algorithm which is the neural network controller. The new algorithm uses the neural network instead of activation function to control object at the output node. Therefore, control object is composed of neural network controller unifying activation function, and it supplies the error back propagation path to calculate the error at the output node. As a result, the input-output pattern problem of the controller which is resigned by the simple structure of neural network is solved, and real-time learning can be possible in general back propagation algorithm. Application of the new algorithm of neural network controller gives excellent performance for initial and tracking response and it shows the robust performance for rapid load change and disturbance. The proposed control algorithm is implemented on a high speed DSP, TMS320C32, for the speed of 3-phase induction motor. Enhanced performance is shown in the test of the speed control.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.301-308
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• 1997

The learning speed of the neural networks, the most important factor in applying to real problems, greatly depends on the learning rate of the networks, Three approaches-empirical, deterministic, and stochastic ones-have been proposed to date. We proposed a new learning-rate selection algorithm using an evolutionary programming search scheme. Even though the performance of our method showed better than those of the other methods, it was found that taking much time for selecting evolutionary learning rates made the performance of our method degrade. This was caused by using static intervals (called static windows) in order to update learning rates. Out algorithm with static windows updated the learning rates showed good performance or didn't update the learning rates even though previously updated learning rates shoved bad performance. This paper introduce a window control scheme to avoid such problems. With the window control scheme, our algorithm try to update the learning ra es only when the learning performance is continuously bad during a specified interval. If previously selected learning rates show good performance, new algorithm will not update the learning rates. This diminish the updating time of learning rates greatly. As a result, our algorithm with the window control scheme show better performance than that with static windows. In this paper, we will describe the previous and new algorithm and experimental results.

• 한국통신학회논문지
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• 제27권2B호
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• pp.171-177
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• 2002

본 논문에서는 최근 장거리 초고속 광통신 시스템에 있어서 해결해야 할 문제로 대두되고 있는 편광모드분산 (PMD) 보상 시스템에 적용할 수 있는 새로운 방식의 편광상태(SOP) 추적 알고리즘에 대해 소개하였다. 새로운 SOP 추적 알고리즘은 본 연구팀에서 기존에 발표한 SOP 추적 알고리즘과 마찬가지로 헤테로다인 코히어런트 수신 방식을 근거로 하고 있으나 기존의 방법과는 달리 수시로 변하는 입력 신호의 SOP에 관계없이 항상 단 세 번만의 측정을 필요로 하기 때문에 동작 시간이 항상 일정하며 속도가 매우 빠른 장점이 있다. 이러한 특성은 매우 안정적이고 빠른 SOP 추적을 요하는 PMD 보상 시스템에 효과적으로 적용될 수 있다.